bert-base-uncased_qa_squad1_houlsby
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camembert-base-squadFR-fquad-piaf
AgentPublic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗roberta-base-cuad
akdeniz27基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗OmniSearch-Qwen-VL-Chat-en
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View details ↗xlm-roberta-large-xquad
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View details ↗finqa_model
anablasi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-mini-finetuned-squad
anas-awadalla基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗question-answering-roberta-base-s-v2
consciousAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-base-uncased-squad-v1
csarronThis model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. This model is case-insensitive: it does not make a difference between english and English.
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View details ↗mobilebert-uncased-squad-v1
csarronMobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
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View details ↗mobilebert-uncased-squad-v2
csarronThis model was fine-tuned from the HuggingFace checkpoint `google/mobilebert-uncased` on SQuAD2.0. > Created by Qingqing Cao | GitHub | Twitter
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View details ↗bert-base-cased-squad2
deepset**Language model:** bert-base-cased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗bert-base-uncased-squad2
deepset基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. **Language model:** bert-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗bert-medium-squad2-distilled
deepset**Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗deberta-v3-base-squad2
deepsetThis is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. **Language model:** deberta-v3-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x NVIDIA A10G Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗deberta-v3-large-squad2
deepsetThis is the deberta-v3-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. **Language model:** deberta-v3-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x NVIDIA A10G Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗electra-base-squad2
deepset**Language model:** electra-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x Tesla v100 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗gelectra-base-germanquad
deepset**Language model:** gelectra-base-germanquad **Language:** German **Training data:** GermanQuAD train set (~ 12MB) **Eval data:** GermanQuAD test set (~ 5MB) **Infrastructure**: 1x V100 GPU **Code:** See an example extractive QA pipeline built with Haystack **Published**: Apr 21st, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗gelectra-large-germanquad
deepset**Language model:** gelectra-large-germanquad **Language:** German **Training data:** GermanQuAD train set (~ 12MB) **Eval data:** GermanQuAD test set (~ 5MB) **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗minilm-uncased-squad2
deepset**Language model:** microsoft/MiniLM-L12-H384-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x Tesla v100 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗roberta-base-squad2
deepsetThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. We have also released a distilled version of this model called deepset/tinyroberta-squad2. It has a comparable prediction quality and runs at twice the speed of deepset/roberta-base-squad2. **Language model:** roberta-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 4x Tesla v100 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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